A handwritten digit classifier using built using a Convolutional Neural Network (CNN). Used various techniques such as data augmentation, batch normalisation, and dropout to improve the model's performance.

Comparing various classification algorithms to predict whether an adult earns more than $50,000 a year. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Comparing various regression algorithms to predict house prices in relation to the distance from the coast. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Practicing various algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning.

Practicing various Reinforcement Learning algorithms and techniques. This includes Q-Learning, Deep Q-Learning, and Asynchronous Advantage Actor-Critic (A3C) algorithms.

Implementing algorithms from scratch such as the Nearest Neighbours algorithm. Requires an understanding of the Mathematics behind the algorithms and the ability to implement them.

Be able to use and implement algorithms, with the Lasso and inductive conformal prediction algorithms as examples.

Be able to use and implement algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples.

Implemented various algorithms and techniques learnt during the course, such as Nearest Neighbours, Conformal Prediction, Regression algorithms, data preprocessing, kernels, Neural Networks, SVMs, etc.

Focusing on learning generative models, using third-party models and using advanced techniques. This includes techniques such as transfer learning, LLM Agents, and Generative Models.